Triggering and Expertise

AI agent skills are essentially procedural knowledge files (often skill.md) that teach an agent how to perform specific tasks. The first hurdle is ensuring the agent actually uses the skill. Because agents load only the name and description into their context window at startup, these must be highly descriptive. Avoid vague names like "generates reports"; instead, explicitly state what the skill does and when it should be triggered. It is often safer to "oversell" the description to prevent the model from under-triggering.

Once triggered, the skill body must contain real-world expertise rather than generic instructions generated by an LLM. The most valuable content to include is "gotchas"—environment-specific facts or corrections you have had to make manually in the past. By documenting these, you prevent the agent from repeating the same mistakes.

Context Management and Determinism

To keep performance high and costs low, manage context window usage by keeping the skill.md body under 5,000 tokens (roughly 500 lines). For larger, complex tasks, use a "progressive disclosure" pattern: move secondary information into a references/ sub-folder that the agent only accesses when necessary.

Crucially, distinguish between tasks that allow for improvisation and those that require precision. For fragile, high-stakes steps—such as mathematical calculations or data reconciliation—avoid relying on the LLM's probabilistic nature. Instead, move these steps into deterministic scripts located in a scripts/ directory. This ensures the logic remains consistent across every execution, effectively removing the "guesswork" from the agent's workflow.

Security and Vetting

Because agent skills can execute code and access local file systems or API keys, they represent a significant security surface. An audit of 4,000 public skills revealed that 35% contained security flaws, with 13% harboring critical issues like malware or prompt injection vulnerabilities. Treat every agent skill as you would an untrusted third-party software dependency: manually audit the code and verify its access permissions before allowing it to run on your machine.